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Risk score stratification of cutaneous melanoma patients based on whole slide images analysis by deep learning
Céline Bossard1,2, Yahia Salhi2, Amir Khammari3
1Pathology Department, IHP Group, Nantes, France.
Background:
There is a need to improve risk stratification of primary cutaneous melanomas to better guide adjuvant therapy. Taking into account that haematoxylin and eosin (HE)-stained tumour tissue contains a huge amount of clinically unexploited morphological informations, we developed a weakly-supervised deep-learning approach, SmartProg-MEL, to predict survival outcomes in stages I to III melanoma patients from HE-stained whole slide image (WSI).
Methods:
We designed a deep neural network that extracts morphological features from WSI to predict 5-y overall survival (OS), and assign a survival risk score to each patient. The model was trained and validated on a discovery cohort of primary cutaneous melanomas (IHP-MEL-1, n = 342). Performance was tested on two external and independent datasets (IHP-MEL-2, n = 161; and TCGA cohort n = 63). It was compared with well-established prognostic factors. Concordance index (c-index) was used as a metric.
Results:
On the discovery cohort, the SmartProg-MEL predicts the 5-y OS with a c-index of 0.78 on the cross-validation data and of 0.72 on the cross-testing series. In the external cohorts, the model achieved a c-index of 0.71 and 0.69 for the IHP-MEL-2 and TCGA dataset respectively. Furthermore, SmartProg-MEL was an independent and the most powerful prognostic factor in multivariate analysis (HR = 1.84, p-value < 0.005). Finally, the model was able to dichotomize patients in two groups-a low and a high-risk group-each associated with a significantly different 5-y OS (p-value < 0.001 for IHP-MEL-1 and p-value = 0.01 for IHP-MEL-2).
Conclusions:
The performance of our fully automated SmartProg-MEL model outperforms the current clinicopathological factors in terms of prediction of 5-y OS and risk stratification of cutaneous melanoma patients. Incorporation of SmartProg-MEL in the clinical workflow could guide the decision-making process by improving the identification of patients that may benefit from adjuvant therapy.
